zhh210
June 18, 2024, 5:00pm
1
I’ve tried a few different approaches to iterate on 1M records with datasets but all seem quite slow . So this time I’m trying the recommended streaming method from the recent post but still it’s extremely slow. It seems for dataset there will always be multiple ways to iterate but most (or all) of them will not yield satisfiable speed.
import s3fs
s3 = s3fs.S3FileSystem()
import datasets as hfdt
from torch.utils.data import DataLoader
SEED = 0
SAMPLE_BUFFER_SIZE=5_000
RECORDS_TO_KEEP= 100_000
TAKE_SIZE = 1_000_000
dt_train = hfdt.load_dataset('parquet', data_files = ['s3://' + i for i in s3.glob('s3://my_s3_bucket/*')], streaming=True)
dt_train = dt_train['train'].shuffle(seed=SEED, buffer_size=SAMPLE_BUFFER_SIZE)
import torch
import pandas as pd
def custom_collate_fn(batch):
# batch = list(filter(lambda x: x is not None, batch)) # Remove None values
batch = [ 0 for i in batch]
return torch.utils.data.dataloader.default_collate(batch) # Use the default collate function
dl_train = DataLoader(dt_train.take(TAKE_SIZE), num_workers=8, prefetch_factor=2, batch_size=128, collate_fn=custom_collate_fn)
from tqdm import tqdm
for sample in tqdm(dl_train):
pass
The s3 folder has 200 parquet files and in total it has 1M rows and 28 columns. However, for such a small dataset, it takes forever to iterate on the data:
Resolving data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [00:07<00:00, 27.81it/s]
5381it [1:24:45, 1.25it/s]
It took almost 2 hours to finish:
real 118m42.879s
user 421m2.584s
sys 0m55.513s
I’m wondering if HF dataset is really feasible for moderate sized dataset?
@zhh210 The post you referenced was mine and I’m able to stream 900+ records per second from the FineWeb dataset while inferencing over the text read in with a linear model.
What type of data are you loading (images, text, tabular) and how big are these files? If increasing the number of workers didn’t increase speed that might be a sign that the bottleneck is the download (or upload on the other end) speed
zhh210
June 20, 2024, 5:36pm
3
@FelixLabelle It was a surprise to me as well. My parquet files (.snappy.parquet) are tabular data with only numeric values and each of the 200 parquet files is just 1.3M.
Ok, in that case I would just try substitutions to debug potential sources of the issue. My guesses of causes in order of likelihood are
Upload speed limitations (the servers you are downloading from)
(tied for first) Download speed limitations
Something specific to your data
Something in your code which is adding time unbeknownst to you
For 1. you can try downloading similar datasets from HF directly and see if you still have the issue. If you don’t I would check upload speed. I don’t use AWS, but certain storage methods can be slower to get data from and would be a bottleneck.
This test could also be indicative for cause 3. If it fails and upload looks OK, I would try streaming a dummy dataset that’s much smaller and seeing if that’s slow as well. If it, then I would start stripping away your dataset to see if it’s a specific column or number of columns causing an issue.
For 2. if you are using a computer just run a download speed test in your browser. I would also turn off any VPNs if you can, those can add time
Cause 4 can be profiled, I would try that if you haven’t already. That would likely be required anyway if you’ve gotten this far debugging.
If that doesn’t narrow down the problem LMK, we can look at the other causes
1 Like
zhh210
June 21, 2024, 4:54pm
5
Tried loading the dataset from local instead of from s3, it does reduce the running time from 2 hours to 1 hour:
CPU times: user 9.21 s, sys: 2.87 s, total: 12.1 s
Wall time: 1h 51s
profiling shows that most of the time were spent on select.poll(), so i guess this means it’s slow reading .snappy.parquet file:
7811it [2:33:58, 1.18s/it]
3624330 function calls (3624329 primitive calls) in 9238.469 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
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